The company will go bankrupt if the company is not able to compete with other companies. Bankruptcy is one of the conditions or risks that must be faced by business people when running a business, a company also experiencing insufficient funds to carry out company activities, and unable to pay off the obligations resulting from the activities carried out by the company. In predicting bankruptcy, several bankruptcy prediction models were found and developed with the formulation of financial data, but each model has its own level of accuracy in certain sectors. In Malaysia itself, there are various sectors that are experiencing financial problems, retail sectors, travel and tourism sectors, agriculture and fisheries sectors, media and entertainment sectors, as well as manufacturing sectors. The purposes of this study is to analyze the effect of net income after tax, total assets, total liabilities, current assets, and current liabilities on bankruptcy prediction among Malaysian companies that impacted during COVID-19 outbreak by using accounting-based bankruptcy prediction model Zmijewski (1984). The type of research is quantitive research by taking financial data of 99 companies that listed in Bursa Malaysia. The period of the financial year is 2018-2021 which is the object of research to obtain accurate data that can be used in this research. Data collected by document analysis techniques in the form of secondary data of the companies’s annual report. The research design is using the type of correlational design. The independent variable that has the highest correkation with the dependent variable is Total Debt variable, while the lowest one in correlation is net income after tax variable. There are a total five hypotheses that have been developed in this research based on literature review and previous study. Therefore, from the results and findings of the study there are whether positive and negative relationship to bankruptcy prediction, there are total debt and total current liabilities. Researcher is recommending to compare and contrast between the financial distress before the pandemic and after the pandemic, expand sampling, use another methodology design, and use various bankruptcy prediction models.